[R] number of weights in multinom ?

Hello, In the function multinom (package nnet), I get the following message after training for a model with 9 inputs and 6 classes (output) : # weights: 66 (50 variable) I understand that there are 50 variables in the model, but I don't understand the number 66. How can we interpret this

Re: [R] number of weights in multinom ?

On 18/11/2014 11:35, Franck Vermet wrote: Hello, In the function multinom (package nnet), I get the following message after training for a model with 9 inputs and 6 classes (output) : # weights: 66 (50 variable) I understand that there are 50 variables in the model, but I don't understand

Re: [R] number of weights in multinom ?

I have the book by W. Venables and B. Ripley, but I didn't find the answer. Here is an explicit example : library(nnet) data(fgl) ir.glm - multinom(type ~., data=fgl) # weights: 66 (50 variable) initial value 383.436526 iter 10 value 259.867465 iter 20 value 184.185706 iter 30 value

[R] number of weights in multinom

Dear colleagues, In the function multinom (package nnet), I get the following message after training for a model with 9 inputs and 6 classes (output) : # weights: 66 (50 variable) I understand that there are 50 variables in the model, but I don't understand the number 66. How can we